Chun, Yongwan

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Yongwan Chun is an Associate Professor in Geospatial Information Sciences. His research interests include:

  • Geographic Information System
  • Geocomputation
  • Geovisualization
  • Spatial statistics and spatial econometrics
  • Migration and migration modeling
  • Network autocorrelation

ORCID page


Recent Submissions

Now showing 1 - 6 of 6
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    Uncertainty in the Effects of the Modifiable Areal Unit Problem under Different Levels of Spatial Autocorrelation: A Simulation Study
    (Taylor & Francis Ltd, 2018-11-13) Lee, Sang-Il; Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 0000-0001-5125-6450 (Griffith, DA); 297769863 (Chun, Y); 14855602 (Griffith, DA); Chun, Yongwan; Griffith, Daniel A.
    The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.
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    Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data
    (Taylor & Francis Ltd, 2019-01-17) Chun, Yongwan; Kwan, Mei-Po; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 0000-0001-5125-6450 (Griffith, DA); 297769863 (Chun, Y); 14855602 (Griffith, DA); Chun, Yongwan; Griffith, Daniel A.
    No abstract available.
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    Environment and Anthropogenic Activities Influence Cetacean Habitat Use in Southeastern Brazil
    (Inter-Research, 2019-05-09) Tardin, R. H.; Chun, Yongwan; Jenkins, C. N.; Maciel, I. S.; Simão, S. M.; Alves, M. A. S.; 0000-0002-4957-1379 (Chun, Y); 297769863 (Chun, Y); Chun, Yongwan
    Investigating the influence of coastal development on marine environments is a priority to maintain healthy seas. Cetaceans are top predators, keystone and umbrella species and thus are good candidate models to evaluate the extent of anthropogenic impacts on coastal habitats. We employed a generalized linear model with spatial eigenvector mapping (SEV-GLM) to understand the influence of environmental and anthropogenic activities on migrant (humpback whale Megaptera novaeangliae) and non-migrant (Bryde’s whale Balaenoptera brydei and common bottlenose dolphin Tursiops truncatus) cetacean habitat use off Cabo Frio, Rio de Janeiro, Brazil. We hypothesized that both environmental and anthropogenic activities influence their habitat use. Data were collected during 118 boat trips between December 2010 and June 2014. The best SEV-GLM predicted humpback whales would increase linearly with distance to coast, with minimum sea surface temperature (SST) around 19.4-19.8°C and maximum SST around 25.5-26°C, with low variations in chlorophyll a (chl a) concentrations. The model also predicted that humpback whales would occur up to 10 km from diving areas, increasing linearly with distance to fishing grounds. The best non-migrant cetacean SEV-GLM predicted that they would occur more frequently around depths from 30-60 m, increasing with low SST and high chl a concentration. For the anthropogenic component, the model predicted that non-migrant cetaceans would occur up to 10 km from fishing grounds. Our study modeled the influence of anthropogenic activities on cetaceans, and indicates specific priority areas for cetacean conservation, contributing at a local and national scale. © Inter-Research 2019
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    Measuring Global Spatial Autocorrelation with Data Reliability Information
    (Routledge) Koo, Hyeongmo; Wong, D. W. S.; Chun, Yongwan; Koo, Hyeongmo; Chun, Yongwan
    Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular SA statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, one is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation.
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    Implementing Moran Eigenvector Spatial Filtering for Massively Large Georeferenced Datasets
    (Taylor And Francis Ltd.) Griffith, Daniel A.; Chun, Yongwan; 0000-0001-5125-6450 (Griffith, DA); 0000-0002-4957-1379 (Chun, Y); 14855602 (Griffith, DA); 297769863 (Chun, Y); Griffith, Daniel A.; Chun, Yongwan
    Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. ©2019 Informa
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    Geovisualizing Attribute Uncertainty of Interval and Ratio Variables: A Framework and an Implementation for Vector Data
    (2017-12-14) Koo, Hyeongmo; Chun, Yongwan; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 14855602 (Griffith, DA); Griffith, Daniel A.
    This is a prototype implementation for attribute uncertainty visualization based on bivariate. Specifically, the uncertainty visualizations implemented based on three different ways. First, an overlaid symbols on a choropleth map (OSCM) strategy is implemented to visualize attribute uncertainty. A choropleth map is used to represent attributes at the ratio scale, and additional overlaid symbols, such as textures (spacing), circles (size), and bars (size), visualize attribute uncertainty Second, a coloring properties to proportional symbols (CPPS) strategy is applied. A proportional symbol map is more appropriate to represent raw counts or frequencies, and attribute uncertainty can be represented by color saturation and color value in the hue-saturation-value (HSV) color model of proportional symbols. Finally, a composite symbols (CS) strategy is utilized to represent the possible range of an attribute value with its confidence interval. Symbols in CS are constructed with three different sizes of symbol overlaid for each individual location. Two of these symbols represent uncertainty by visualizing the upper and lower limits of attribute values for a given confidence level. Thus, the CS strategy allows users to directly compare uncertainties with corresponding attribute values and their confidence intervals. The ESRI ArcGIS add-in installation file is compatible with ArcGIS 10.x, and developed in .NET framework 4.5 and ArcObject 10.5. It requires Microsoft Windows Vista or higher.

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